Identifying transfer of inquiry skills across physical science simulations using educational data mining

Michael Sao Pedro, Yang Jiang, Luc Paquette, Ryan S. Baker, Janice Gobert

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations

Abstract

Students conducted inquiry using simulations within a rich learning environment for 4 science topics. By applying educational data mining to students' log data, assessment metrics were generated for two key inqury skills, testing stated hypotheses and designing controlled experiments. Three models were then developed to analyze the transfer of these inquiry skills between science topics. Model one, Classic Bayesian Knowledge Tracing, assumes that either complete transfer of skill occurs or no transfer occurs; model two (BKTPST), an extension of BKT, assumes partial transfer and tests that assumption; and model three, a variant of BKT-PST, assumes no transfer and tests this assumption. An analysis of models one and two suggest that transfer of these inquiry skills across topics did occur. This work makes contributions to methodological approaches for measuring fine-grained skills using log files, as well as to the literature on the domain-specificity vs. domain-generality of inquiry skills.

Original languageEnglish (US)
Pages (from-to)222-229
Number of pages8
JournalProceedings of International Conference of the Learning Sciences, ICLS
Volume1
Issue numberJanuary
StatePublished - 2014
Externally publishedYes
Event11th International Conference of the Learning Sciences: Learning and Becoming in Practice, ICLS 2014 - Boulder, United States
Duration: Jun 23 2014Jun 27 2014

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Education

Fingerprint

Dive into the research topics of 'Identifying transfer of inquiry skills across physical science simulations using educational data mining'. Together they form a unique fingerprint.

Cite this